{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import and setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------- import\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------- figure size\n",
    "plt.rcParams['figure.figsize'] =[8, 6]\n",
    "\n",
    "# ---------- axes\n",
    "plt.rcParams['axes.grid'] = True\n",
    "plt.rcParams['axes.linewidth'] = 1.5\n",
    "\n",
    "# ---------- ticks\n",
    "plt.rcParams['xtick.direction'] = 'in'\n",
    "plt.rcParams['ytick.direction'] = 'in'\n",
    "plt.rcParams['xtick.major.width'] = 1.0\n",
    "plt.rcParams['ytick.major.width'] = 1.0\n",
    "plt.rcParams['xtick.major.size'] = 8.0\n",
    "plt.rcParams['ytick.major.size'] = 8.0\n",
    "\n",
    "# ---------- lines\n",
    "plt.rcParams['lines.linewidth'] = 2.5\n",
    "\n",
    "# ---------- grid\n",
    "plt.rcParams['grid.linestyle'] = ':'\n",
    "\n",
    "# ---------- font\n",
    "plt.rcParams['font.size'] = 20\n",
    "#plt.rcParams['pdf.fonttype'] = 42    # embed fonts in PDF using type42 (True type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pkl(filename):\n",
    "    with open(filename, 'rb') as f:\n",
    "        return pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Spg_num</th>\n",
       "      <th>Spg_sym</th>\n",
       "      <th>Spg_num_opt</th>\n",
       "      <th>Spg_sym_opt</th>\n",
       "      <th>E_eV_atom</th>\n",
       "      <th>Magmom</th>\n",
       "      <th>Opt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>Pna2_1</td>\n",
       "      <td>33</td>\n",
       "      <td>Pna2_1</td>\n",
       "      <td>-3.397373</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>80</td>\n",
       "      <td>I4_1</td>\n",
       "      <td>80</td>\n",
       "      <td>I4_1</td>\n",
       "      <td>-3.288571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>62</td>\n",
       "      <td>Pnma</td>\n",
       "      <td>62</td>\n",
       "      <td>Pnma</td>\n",
       "      <td>-3.180439</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>156</td>\n",
       "      <td>P3m1</td>\n",
       "      <td>156</td>\n",
       "      <td>P3m1</td>\n",
       "      <td>-2.858680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>105</td>\n",
       "      <td>P4_2mc</td>\n",
       "      <td>105</td>\n",
       "      <td>P4_2mc</td>\n",
       "      <td>-2.778002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>111</td>\n",
       "      <td>P-42m</td>\n",
       "      <td>123</td>\n",
       "      <td>P4/mmm</td>\n",
       "      <td>-2.733486</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47</td>\n",
       "      <td>Pmmm</td>\n",
       "      <td>47</td>\n",
       "      <td>Pmmm</td>\n",
       "      <td>-2.511932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>213</td>\n",
       "      <td>P4_132</td>\n",
       "      <td>213</td>\n",
       "      <td>P4_132</td>\n",
       "      <td>-2.452212</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>221</td>\n",
       "      <td>Pm-3m</td>\n",
       "      <td>221</td>\n",
       "      <td>Pm-3m</td>\n",
       "      <td>-2.302506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>131</td>\n",
       "      <td>P4_2/mmc</td>\n",
       "      <td>131</td>\n",
       "      <td>P4_2/mmc</td>\n",
       "      <td>-1.986213</td>\n",
       "      <td>NaN</td>\n",
       "      <td>done</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Spg_num   Spg_sym  Spg_num_opt Spg_sym_opt  E_eV_atom  Magmom   Opt\n",
       "2       33    Pna2_1           33      Pna2_1  -3.397373     NaN  done\n",
       "9       80      I4_1           80        I4_1  -3.288571     NaN  done\n",
       "6       62      Pnma           62        Pnma  -3.180439     NaN  done\n",
       "3      156      P3m1          156        P3m1  -2.858680     NaN  done\n",
       "8      105    P4_2mc          105      P4_2mc  -2.778002     NaN  done\n",
       "4      111     P-42m          123      P4/mmm  -2.733486     NaN  done\n",
       "0       47      Pmmm           47        Pmmm  -2.511932     NaN  done\n",
       "7      213    P4_132          213      P4_132  -2.452212     NaN  done\n",
       "5      221     Pm-3m          221       Pm-3m  -2.302506     NaN  done\n",
       "1      131  P4_2/mmc          131    P4_2/mmc  -1.986213     NaN  done"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rslt_data = load_pkl('./pkl_data/rslt_data.pkl')\n",
    "\n",
    "# ---------- sort by Energy\n",
    "rslt_data.sort_values(by=['E_eV_atom']).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data: 10\n",
      "Success: 10\n",
      "Error: 0\n",
      "Emin: -3.3973728125 eV/atom\n"
     ]
    }
   ],
   "source": [
    "# ---------- Number of structures\n",
    "ndata = len(rslt_data)\n",
    "print('Number of data: {}'.format(ndata))\n",
    "\n",
    "# ---------- check success and error\n",
    "nsuccess = rslt_data['E_eV_atom'].count()\n",
    "nerror = ndata - nsuccess\n",
    "print('Success: {}'.format(nsuccess))\n",
    "print('Error: {}'.format(nerror))\n",
    "\n",
    "# ---------- minimum\n",
    "Emin = rslt_data['E_eV_atom'].min()\n",
    "print('Emin: {} eV/atom'.format(Emin))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Energy vs. trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Energy (eV/atom)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "# ---------- axis\n",
    "dx = 1\n",
    "ax.set_xlim([0, ndata+dx])\n",
    "ax.set_ylim([-0.5, 3])\n",
    "\n",
    "# ---------- hline at zero\n",
    "ax.hlines(0.0, -dx, ndata+dx, 'k', '--')\n",
    "\n",
    "# ---------- plot\n",
    "# x <-- ID + 1\n",
    "ax.plot(rslt_data.index + 1, rslt_data['E_eV_atom'] - Emin, 'o', ms=15, mew=2.0, alpha=0.8)\n",
    "\n",
    "# ---------- title and label\n",
    "ax.set_title('Random search for Na$_8$Cl$_8$')\n",
    "ax.set_xlabel('Number of trials')\n",
    "ax.set_ylabel('Energy (eV/atom)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ---------- save figure\n",
    "fig.savefig('Na8Cl8_RS.png', bbox_inches='tight')    # PNG\n",
    "#fig.savefig('title_RS.png', bbox_inches='tight', dpi=300)    # high dpi PNG\n",
    "#fig.savefig('title_RS.pdf', bbox_inches='tight')    # PDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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